A social platform for movie enthusiasts to explore, discuss, and review films. Designed to be your one-stop destination for all movie-related needs, offering a superior user experience and unparalleled depth of content.

Tvflix is a simple and responsive web app built using Vanilla JS, leveraging the power of Postman and the TMDB API to seamlessly fetch and display comprehensive movie details. This project serves as a template for larger applications.

The 'MOVICO' project is a 'Movie Recommendation System'. It is an 'Artificial Intelligence-Machine Learning' project. Specifically, it is a 'Movie Recommendation System' that uses 'Collaborative Filtering Techniques'. The project 'Movie-Recommendation-System-MOVICO' was created as a project for the course 'Machine Intelligence', 'ue20cs302'.

AI powered movie recommendation system using vector search and cosine similarity. The system takes user input in the form of a movie title or overview and returns the most similar movies based on their embeddings.

A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user Topics

Recommendo is a movie recommendation system developed using Flask, HTML, Bootstrap, JavaScript, and MySQL. The system allows users to search for movie recommendations and get personalized suggestions based on their favorite movies.

The Movie Recommendation System using KNN (K-Nearest Neighbors) algorithm is a collaborative filtering-based system that suggests movies to users based on similar users' preferences and viewing patterns. The system analyzes user-movie ratings to find users with similar taste patterns and recommends movies that these similar users have enjoyed.

Developed a Movie Recommendation System using content-based filtering and collaborative filtering to suggest personalized movie recommendations.

The Movie Information App is a Flutter-based mobile application that provides movie recommendations by utilizing the TMDB (The Movie Database) API. The app allows users to explore movies, view detailed information, and get recommendations based on their interests.

Movie recommendation system using Machine learning

This projects serves as a web app to showcase the recommendation engine project

FilmFinder is a personalized movie recommendation system that suggests films based on user preferences using machine learning algorithms. Integrated into a Streamlit web-app, it offers detailed insights on over 5,000 movies, including summaries, ratings, and genres, to enhance user viewing choices.

This Repo contains The Implementation of Content-Based Recommended System and Collaborative Filtering Recommended System for movies dataset in python.

This project is a Movie Recommendation System that suggests movies to users based on their input of a favorite movie. It uses Cosine Similarity and TF-IDF Vectorizer to compute similarity between movies based on features like genres, keywords, cast, crew, and more.

Explore our collection of articles and resources on safely downloading the latest movies, including reviews, streaming options, and legal download platforms. Stay updated with step-by-step guides, trending films, and low-competition keywords for movie enthusiasts.

CineBrain uses ML & NLP to analyze movies and recommend similar ones based on user preferences through cosine similarity.

This repository contains a robust and user-friendly movie recommendation system built to enhance your movie-watching experience. Whether you're a film enthusiast or just looking for something new to watch, our system has you covered topics

This project it's a movie recommendation engine composed of a custom NCF model to predict user preferences and generate personalized recommendations for large-scale user-item interaction data.

An React app made with tailwind css, where you can find any movies details, it's imdb or the description.

FLIX-HUB is a movie recommendation system utilizing the Netflix dataset. It features comprehensive data preprocessing and analysis, generating personalized movie and TV show suggestions based on TF-IDF vectorization and cosine similarity. The project includes interactive visualizations for insights into content trends and distributions.